Comparing Computer Versus Human Data Collection Methods for Public Usability Evaluations of a Tactile-Audio Display
Bibliographic record
Abstract
We present a public usability study that provides preliminary results on the effectiveness of a universally designed system that conveys music and other sounds into tactile sensations. The system was displayed at a public science museum as part of a larger multimedia exhibit aimed at presenting a youths’ perspective on global warming and the environment. We compare two approaches to gathering user feedback about the system in a study that we conducted to assess user responses to the inclusion of a tactile display within the larger audio-visual exhibit; in one version, a human researcher administered the study and in the other version a touch screen computer was used to obtain responses. Both approaches were used to explore the public’s basic understanding of the tactile display within the context of the larger exhibit. The two methods yielded very similar responses from participants; however, our comparison of the two techniques revealed that there were subtle differences overall. In this paper, we compare the two study techniques for their value in providing access to public usability data for assessing universally designed interactive systems. We present both sets of results, with a cost benefit analysis of using each in the context of public usability tests for universal design.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.114 | 0.270 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".